Operations Research and Machine Learning
For optimization to make sense, we as users, may want to specify relative importants of transactions. In the end we want to deduce a retroactive savings plan, and to do so we need to “cancel” some transactions. It is clear that one may save on grocery shopping, but saving on rent payments does not sound like a good idea.
In this part we visualize bank transactions data, aiming to get an idea
In this part we explore our bank account data, prepare it and deal with the transaction classification.
This sunbirst chart from plotly library is a sneek preview from visualization part of this post series. Our task for now is to prepare data, in order to allow such type of visualizations.
I was once analyzing my bank account spendings. My bank UI was not very convenient: I cannot easily filter on interesting fields, some visualization were lacking, etc. But was there is an export as .csv button, which came quite handy. I decided to look at my expenses through my favorite tools.
Photo by Morgan Housel on Unsplash
Nice! I can do whatever I want with my data! I started looking where I could have avoided spending to much money and then an idea came to my mind: this can be treated as optimization problem! So here it is: Budget optimization @HOME
Original photo by Kalden Swart on Unsplash
During my career I encountered Operations Research (O.R.) tooling in various environments: banking, supply chains, e-commerce and even fishery industries. In most of the cases I felt surprized (and sometimes frustrated) that our customers know very little to nothing about Operations Research discipline, while almost everyone knew what AI, DS and ML mean. In the best case people will assume that this is something to do with machine learning (and they will be partially correct), in the worst case they will guess that this is something to do with operations management (also not totally wrong).